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The quantile‐based classifier with variable‐wise parameters

Marco Berrettini et al.

Canadian Journal of Statistics2025https://doi.org/10.1002/cjs.11837article
ABDC A
Weight
0.41

Abstract

Quantile‐based classifiers can classify high‐dimensional observations by minimizing a discrepancy of an observation to a class based on suitable quantiles of the within‐class distributions, corresponding to a unique percentage for all variables. The present work extends these classifiers by introducing a way to determine potentially different optimal percentages for different variables. Furthermore, a variable‐wise scale parameter is introduced. A simple greedy algorithm to estimate the parameters is proposed. Their consistency in a nonparametric setting is proved. Experiments using artificially generated and real data confirm the potential of the quantile‐based classifier with variable‐wise parameters.

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https://doi.org/https://doi.org/10.1002/cjs.11837

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@article{marco2025,
  title        = {{The quantile‐based classifier with variable‐wise parameters}},
  author       = {Marco Berrettini et al.},
  journal      = {Canadian Journal of Statistics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/cjs.11837},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.